NeuroPerf: Neuromorphic Benchmarking and AI-Optimized Performance Engineering Beyond Von Neumann
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I2P124Keywords:
Neuromorphic Computing, Benchmarking, AI Optimization, Non-Von Neumann Architecture, Spiking Neural Networks, Performance Engineering, NeuroPerf, Parallel Processing, Hardware Acceleration, Cognitive ComputingAbstract
Neuromorphic computing, whose operational principles mimic those of the parallel, event-driven human brain, offers a revolutionary change to energy-efficient, context-aware, and dynamically scalable processing models. Unfortunately, as these systems move away from the linear computation paradigms, traditional benchmarking methods are incapable of revealing their performance potential. To address this challenge, NeuroPerf is introduced to be an AI-optimized benchmarking framework for the new era of computation. It moves beyond the conventional performance criteria by considering neural-inspired metrics such as spiking efficiency, synaptic adaptability, and latency-energy correlation parameters that are beyond FLOPS and throughput. NeuroPerf, with AI-driven optimization, understands workload patterns, modifies benchmarks on various neuromorphic processors, and facilitates the comparability of different platforms without strict standardization. This strategy not only leads to rapid innovation in chip design but also makes a close connection between the hardware capabilities and the goals of cognitive computing. NeuroPerf, by integrating benchmarking of the hardware with the attributes of the architectures, creates a living performance ecosystem that next-generation processors can benefit from. To put it simply, it constitutes a paradigm shift from static testing to intelligent performance engineering, thus becoming a foundation for the evaluation and guidance of future processors in a world that is increasingly beyond Von Neumann.
References
[1] Lux, Narges, et al. "HPC-AI benchmarks-a comparative overview of high-performance computing hardware and AI benchmarks across domains." J. Artif. Intell. Robot. 1 (2024): 2.
[2] Domke, Jens, et al. "Matrix engines for high performance computing: A paragon of performance or grasping at straws?." 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2021.
[3] Sankar, Thambireddy, Reddy Bussu Venkata Ramana, and Anbalagan Balamuralikrishnan. "AI-Optimized Hyperscale Data Centers: Meeting the Rising Demands of Generative AI Workloads." International Journal of Trend in Scientific Research and Development 7.1 (2023): 1504-1514.
[4] Areo, Gideon. "Enhancing FINFET-Based VLSI Circuits Through AI-Optimized Power and Performance Metrics in Cross-Cloud DevOps Environments." (2024).
[5] Alloun, Wiem, and Cinzia Calvio. "Bio-driven sustainable extraction and ai-optimized recovery of functional compounds from plant waste: A comprehensive review." Fermentation 10.3 (2024): 126.
[6] Rojek, Izabela, et al. "AI-optimized technological aspects of the material used in 3D printing processes for selected medical applications." Materials 13.23 (2020): 5437.
[7] Boutros, Andrew, et al. "Beyond peak performance: Comparing the real performance of AI-optimized FPGAs and GPUs." 2020 international conference on field-programmable technology (ICFPT). IEEE, 2020.
[8] Cisbani, Evaristo, et al. "AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case." Journal of Instrumentation 15.05 (2020): P05009.
[9] Kumar, P. Vishnu, et al. "AI-Optimized hardware design for Internet of Things (IoT) devices." 2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST). IEEE, 2024.
[10] Dittakavi, Raghava Satya SaiKrishna. "AI-optimized cost-aware design strategies for resource-efficient applications." Journal of Science & Technology 4.1 (2023): 1-10.
[11] Yik, Jason, et al. "Neurobench: Advancing neuromorphic computing through collaborative, fair and representative benchmarking." arXiv 2023 (2023): 2304-04640.
[12] Ostrau, Christoph, et al. "Benchmarking neuromorphic hardware and its energy expenditure." Frontiers in neuroscience 16 (2022): 873935.
[13] Parakala, Adityamallikarjunkumar. "Building a Resilient Automation Ecosystem: Architecture, Governance, and Teamwork." International Journal of Emerging Research in Engineering and Technology 5.3 (2024): 84-96.
[14] Kulkarni, Shruti R., et al. "Benchmarking the performance of neuromorphic and spiking neural network simulators." Neurocomputing 447 (2021): 145-160.
[15] Vineyard, Craig M., et al. "Benchmarking event-driven neuromorphic architectures." Proceedings of the International Conference on Neuromorphic Systems. 2019.
[16] Guntupalli, Bhavitha. "How I Optimized a Legacy Codebase with Refactoring Techniques." International Journal of Emerging Trends in Computer Science and Information Technology 3.1 (2022): 98-106.
[17] Narduzzi, Simon, et al. "Benchmarking Neuromorphic Computing for Inference." Industrial Artificial Intelligence Technologies and Applications. River Publishers, 2023. 1-19.
